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Global surface-ocean partial pressure of carbon dioxide (pCO2) estimates from a machine learning ensemble: CSIR-ML6 v2019a (NCEI Accession 0206205)

Metadata Updated: December 1, 2023

This dataset contains surface-ocean partial pressure of carbon dioxide (pCO2) that the ensemble mean of six two-step clustering-regression machine learning methods. The ensemble is a combination of two clustering approaches and three regression methods. For the clustering approaches, we use K-means clustering (21 clusters) and open ocean CO2 biomes as defined by Fay and McKinley (2014). Three machine learning regression methods are applied to each of these two clustering methods. These machine learning methods are feed-forward neural-network (FFN), support vector regression (SVR) and gradient boosted machine using decision trees (GBM). The final estimate of surface ocean pCO2 is the average of the six machine learning estimates resulting in a monthly by 1° ⨉ 1° resolution product that extends from the start of 1982 to the end of 2016. Sea-air fluxes (FCO2) calculated from pCO2 are also presented in the data. The discrete boundaries of the clustering approach result in semi-discrete discontinuities in pCO2 and fCO2 estimates. These are smoothed by applying a 3 ⨉ 3 ⨉ 3 convolution (moving average) to the dataset in time, latitude and longitude.

Access & Use Information

License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Date 2023-08-30T12:58:57Z
Metadata Created Date March 19, 2021
Metadata Updated Date December 1, 2023
Reference Date(s) November 5, 2019 (publication)
Frequency Of Update asNeeded

Metadata Source

Harvested from NOAA/NESDIS/ncei/accessions

Graphic Preview

Preview graphic

Additional Metadata

Resource Type Dataset
Metadata Date 2023-08-30T12:58:57Z
Metadata Created Date March 19, 2021
Metadata Updated Date December 1, 2023
Reference Date(s) November 5, 2019 (publication)
Responsible Party (Point of Contact)
Contact Email
Guid gov.noaa.nodc:0206205
Access Constraints Cite as: Gregor, Luke; Lebehot, Alice D.; Kok, Schalk; Monteiro, Pedro M. S. (2019). Global surface-ocean partial pressure of carbon dioxide (pCO2) estimates from a machine learning ensemble: CSIR-ML6 v2019a (NCEI Accession 0206205). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/z682-mn47. Accessed [date]., Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose.
Bbox East Long 180
Bbox North Lat 89.5
Bbox South Lat -89.5
Bbox West Long -180
Coupled Resource
Frequency Of Update asNeeded
Graphic Preview Description Preview graphic
Graphic Preview File https://www.ncei.noaa.gov/access/metadata/landing-page/bin/gfx?id=gov.noaa.nodc:0206205
Graphic Preview Type PNG
Harvest Object Id 660388d9-3594-441a-8f64-7441e59c03d9
Harvest Source Id c084a438-6f6b-470d-93e0-16aeddb9f513
Harvest Source Title NOAA/NESDIS/ncei/accessions
Licence accessLevel: Public
Lineage
Metadata Language eng
Metadata Type geospatial
Old Spatial {"type": "Polygon", "coordinates": [[[-180.0, -89.5], [180.0, -89.5], [180.0, 89.5], [-180.0, 89.5], [-180.0, -89.5]]]}
Progress completed
Spatial Data Service Type
Spatial Reference System
Spatial Harvester True
Temporal Extent Begin 1982-01-01
Temporal Extent End 2016-12-31

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